With the rapid development of modern traffic network, it is very important to accurately predict the trajectory of key traffic nodes. Aircraft navigation path, as a kind of data with complex temporal characteristics, is often affected by climate change and environmental disturbance, which makes the dynamic prediction of path characteristics a challenge. In this context, a novel trajectory prediction technique combining LSTM model is introduced in this study. The technique achieves accurate prediction of the target path through deep training of a large amount of data. The experimental results fully demonstrate the significant advantages of this method compared with traditional algorithms in the accuracy of flight target prediction and data processing efficiency.
The research on robustness optimization of large-scale heterogeneous combat network(HCN) is of great significance to improve the survival ability of combat system in complex battlefield and ensure that it can function even when some of its components fail. Most of the existing optimization algorithms are applied in homogeneous networks, and their application to HCNs invariably leads to information loss. In order to fill this gap, this study presents a robustness optimization algorithm, known as the HCN-MOEA, which takes into account the characteristics of the heterogeneity of HCN by introducing the functional robustness of HCN. What’s more, we design the heterogeneous mutation operator which removes the edges that are impossible in actual network to reduce the search space and improves the convergence speed. Finally, We execute optimization experiment and comparative experiment to verify the effectiveness of the optimization objective and mutation operator of HCN-MOEA, which proves that it is superior to the existing algorithms in the application of large-scale HCN. Additionally, the optimization results obtained by the experiment can also provide support for building a combat network with better combat capability.
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